论文标题
学会积极减少机器人控制任务的内存需求
Learning to Actively Reduce Memory Requirements for Robot Control Tasks
论文作者
论文摘要
配备了丰富的感应方式（例如RGB-D摄像头）执行长马功能任务的机器人激发了对高度记忆效率的策略的需求。控制机器人的最先进方法通常使用对任务过度富裕或依靠手工制作的技巧来提高内存效率的内存表示。相反，这项工作为共同综合内存表示和政策提供了一种通用方法。由此产生的政策积极寻求减少记忆需求。具体而言，我们提出了一个强化学习框架，该框架利用了套索正规化的实现，以合成采用低维和以任务为中心的内存表示的策略。我们通过模拟示例（包括离散和连续空间中的导航以及基于视觉的室内导航设置在光真实的模拟器中）证明了方法的功效。这些示例的结果表明，我们的方法能够找到仅依赖低维内存表示，改善概括并积极减少内存需求的策略。
Robots equipped with rich sensing modalities (e.g., RGB-D cameras) performing long-horizon tasks motivate the need for policies that are highly memory-efficient. State-of-the-art approaches for controlling robots often use memory representations that are excessively rich for the task or rely on hand-crafted tricks for memory efficiency. Instead, this work provides a general approach for jointly synthesizing memory representations and policies; the resulting policies actively seek to reduce memory requirements. Specifically, we present a reinforcement learning framework that leverages an implementation of the group LASSO regularization to synthesize policies that employ low-dimensional and task-centric memory representations. We demonstrate the efficacy of our approach with simulated examples including navigation in discrete and continuous spaces as well as vision-based indoor navigation set in a photo-realistic simulator. The results on these examples indicate that our method is capable of finding policies that rely only on low-dimensional memory representations, improving generalization, and actively reducing memory requirements.
